The name is absent



30


Stata Technical Bulletin


STB-57


logit (N=753): Factor Change in Odds
Odds of: inLF vs NotInLF

Ifp I

b

z

P>z

e^b

e^bStdX

SDofX

kε I

-1.46291

-7.426

0.000

0.2316

0.4646

0.5240

k618 I

-0.06457

-0.950

0.342

0.9375

0.9183

1.3199

age I

-0.06287

-4.918

0.000

0.9391

0.6020

8.0726

wc I

0.80727

3.510

0.000

2.2418

1.4381

0.4500

he I

0.11173

0.542

0.588

1.1182

1.0561

0.4885

Iwg I

0.60469

4.009

0.000

1.8307

1.4266

0.5876

inc I

-0.03445

-4.196

0.000

0.9661

0.6698

11.6348

b =
z =

P>z =

raw coefficient
z-score for test of
р-value for z-test

b=0

e^b = exp(b) = factor change in odds for unit increase in X

e^bStdX = exp(b*SD of X) = change in odds for SD increase in X
SDofX = standard deviation of X

Alternatively, a user might want to interpret the coefficients in terms of their effect on the latent y* that underlies the observed
variable
y. Note that to use Iistcoef to compute standardized coefficients does not require that the model be reestimated.

. Iistcoef, std help

logit (N=753): Unstandardized and Standardized Estimates
Observed SD: .49562951
Latent SD: 2.0500391

Odds of: inLF vs NotInLF

Ifp I

b

z

P>z

bStdX

bStdY

bStdXY

SDofX

k5 I

-1.46291

-7.426

0.000

-0.7665

-0.7136

-0.3739

0.5240

k618 I

-0.06457

-0.950

0.342

-0.0852

-0.0315

-0.0416

1.3199

age I

-0.06287

-4.918

0.000

-0.5075

-0.0307

-0.2476

8.0726

we I

0.80727

3.510

0.000

0.3633

0.3938

0.1772

0.4500

he I

0.11173

0.542

0.588

0.0546

0.0545

0.0266

0.4885

Iwg I

0.60469

4.009

0.000

0.3553

0.2950

0.1733

0.5876

inc I

-0.03445

-4.196

0.000

-0.4008

-0.0168

-0.1955

11.6348

b = raw coefficient

z = z-score for test of b=0

P>z = р-value for z-test

bStdX = х-standardized coefficient
bStdY = у-standardized coefficient

bStdXY = fully standardized coefficient
SDofX = standard deviation of X

Example with mlogit

A key to fully interpreting the multinomial logit model is to consider all contrasts among the outcome categories. The
standard output from mlogit provides contrast between all outcomes and the category specified by basecategory. Here we
specify the rrr option in order to obtain “relative risk ratios” which are also known as factor change coefficients.

. mlogit occ white ed exper, basecategory(1) rrr nolog

Multinomial regression

Log likelihood = -426.80048

Number of obs =
LR chi2(12)

Prob > chi2     =

Pseudo R2       =

337
166.09
0.0000
0.1629

occ I

RRR

Std. Err.

z

P>z

[957. Conf.

Interval]

BlueCol I
white I
ed I
exper I

3.443553
.9053581
1.004732

2.494631
.0926011
.0174807

1.707

-0.972

0.271

0.088

0.331

0.786

.8324658

.7408981

.9710484

14.2445

1.106324

1.039585

Craft     I

white I
ed I
exper I
---------+

1.603748

1.098357

1.028071

.9691607

.1071502

.0171417

0.782

0.962

1.660

0.434

0.336

0.097

.4906218

.9072032

.9950164

5.242345

1.329788

1.062223



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